Many of the decisions that Internet Protocol network operators make depend on how the traffic flows in and through their network. When used together with routing information, information on how the traffic flows across networks gives network operators valuable information about the current network state, which can be instrumental in traffic engineering, network management, provisioning, and making important business decisions
Apart from IP networks, volume of data flowing through and/or between telecommunication networks and knowledge of attributes of such data can be also crucial for competing telecommunication networks or for entities for which such data can be useful for optimization and analysis of network layout/structure, business decisions, prospect identification for further improvement thereof.
For inferring how traffic flows in a network, typically a traffic matrix is required as an input. A traffic matrix (TM) describes the amount of data traffic that is transmitted between each pair of ingress and egress points in a network. When used together with the routing information, a traffic matrix can give a network operator valuable information about the current network state.
In IP networks, computing effective ranks of TM's is an important tool in numerical analysis and traffic engineering, wherein an effective rank indicates the reduction in dimensionality of a linear system. A TM having a low effective rank suggests that a smaller number of rows (or columns) are sufficient to adequately predict the entire TM by virtue of the rows (or columns) being linearly correlated to each other thereby allowing prediction of the entire TM.
There are often times when a network, at a defined level of aggregation such as at a prefix level or at an atom level is not able to view traffic passing through another network as it is not directly passing through it and is therefore not observable. At the same time, in many situations it is desirable to form an estimate for traffic that is not directly observable so that efficient load balancing and traffic engineering activities can be performed along with monitoring and using the traffic flow trends across AS'es, ISP's, or Customers of ISP's. Further, an improved knowledge of how traffic flows through the Internet as a whole can inform our understanding of how demand, topology, and economics interact to shape Internet evolution.
Similarly, data flowing through telecommunication networks can help stakeholders understand the data flow pattern, inter network linkages, customers calling profile. It is therefore helpful if, based on a partial set of data/traffic available, an entity or a customer can estimate the volume to data flowing through another entity or network and also understand the attributes of such data so as to determine meaningful information.
Further, it would also be important to infer invisible TM elements by allowing an Autonomous System/Network (AS) to predict the amount the invisible traffic passing through other pairs of AS'es/Networks. Currently, no solutions and/or frameworks are present for a predictor network that aims at estimating and inferring information about data/traffic that flows through a target network and is invisible to the predictor network. Typically, currently available works for deducing missing TM elements focus on the time based estimation and are therefore temporal in nature and not spatial as is desired in the above mentioned problem statement. Further, as the correlation between temporally sequenced TM's is strong, they focus on determining network's internal measurements rather than focusing on measuring traffic flowing through or across other networks.
Generally, most existing TM matrix completion methods work on a set of strong assumptions such as a need for the matrices to be uniformly sampled, no irregular scattering of usable visible elements, and low variability in TM elements. These assumptions do not hold good in the mentioned problem statement as the elements in TM matrices across network's or AS'es would have high variability and irregular scattering by nature of the traffic flow.
Further, most known traffic estimation mechanisms such as gravity models use rank-1 or rank-2 models instead of working on TM's having low effective rank. Further these mechanisms are concerned only with TMs within a single network and do not explicitly disclose the low effective rank of the TM's they examine. There is therefore a need for an efficient system and method for inferring traffic information flowing through a target network using a predictor network, wherein the inferred traffic information does not flow through the predictor network.